The PhD students will be part of a dynamic, multidisciplinary research team from UMCG including other (PhD) students, technical medicine graduates, image analysis researchers, and clinical researchers. Supervision will be carried out by experts in their respective fields.

PhD students are registered within the Graduate School of Medical Sciences in Groningen, and graduate with a PhD degree from the University of Groningen, Faculty of Medicine.

- Candidates with a Master’s degree in Technical Medicine, Biomedical Engineering, Physics, Computer science or related field
- Experience with deep neural networks
- Experience in TensorFlow
- Technical proficiency in Python and C++
- Aware of recent developments in deep learning
- Experience with image processing, and/or medical imaging is preferable
- Excellent communication skills in English
- Flexible and collaborative attitude
- Available to start the PhD project on short notice

The UMCG has a preventive Hepatitis B policy. The UMCG can provide you with the vaccination, should it be required for your position.

In case of specific professions a ‘Certificate of Good Conduct’ is required.

- A fulltime PhD position (36 hours). First appointment for one year. If candidate is proven suitable, the appointment will be extended for a total of 4 years.
- Salary according to the Dutch scale for PhD project research € 2.422,- gross per month in the first year up to a maximum of € 3.103,- gross per month in the last year.
- A holiday allowance of 8% of the gross annual salary and a year-end bonus of 8.3%
- Minimum of 23 holidays per year in case of fulltime employment.

More information
For more information about this vacancy you may contact:
dr. ir. P.M.A. van Ooijen, Associate Prof Medical Imaging Informatics, dept. of Radiation Therpay; Coordinator Machine Learning Lab, Data Science Center in Health, e-mailadress: p.m.a.van.ooijen@umcg.nl (please do not use the e-mailadress for applications)

Radiation Oncology

The University Medical Centre Groningen, located in the center of Groningen, is one of the Netherlands’ eight university medical centers and the largest employer in the Northern Netherlands. It has an ambitious, dynamic, international environment with state-of-the-art facilities. More than 13,000 employees provide patient care, are involved in medical education and perform cutting-edge scientific research, focused on healthy ageing. Data science and large-scale studies are important focal points in Groningen.
The Department of Radiation Oncology in the UMCG is the first in the Netherlands with an integrated state of the art photon-proton clinic. Currently, radiotherapy treatments are provided for over 4,500 patients annually. The department is part of the UMC Groningen Comprehensive Cancer Center, with 21 multidisciplinary tumor boards, and provides the highest level of oncologic services. The department is actively involved in patient care, research, education and training. The UMC Groningen Proton Therapy Center, part of our Department, started to treat patients with IBA pencil beam scanning technology in January 2018.

Project background
Detection and Prediction of Radiation Toxicity and Tumor Response using Deep Learning - Towards Personalized Adaptive Radiotherapy

Artificial intelligence and deep learning in particular are increasingly used in medical image analysis across a broad range of medical fields. Applications vary from purely diagnostic in radiology, dermatology or pathology, to streamlining workflows or even predicting treatment outcomes in radiotherapy. Radiotherapy treatment for cancer is highly image driven: the location and volume of the tumor are determined precisely using CT, PET and/or MR images, after which an irradiation plan for the treatment is made.

In recent research projects we have shown that image features from pre-treatment CT, PET and MR images are predictive for risk of complications and tumor recurrences after treatment. In this study, we will investigate whether deep learning can be used to identify image features that can predict the risk of tumor recurrences and complications after treatment.

From a technical perspective, this project presents a challenging task that requires the use of state-of-the-art algorithms. In particular, how best to handle 3D image sets generated by CT and MR is an open question in the field. From a clinical perspective, if accurate predictions are feasible an optimal treatment plan could be made for each patient, which will help to maximize both survival and quality of life after treatment for cancer patients.

Applications are invited for 1 PhD position within the project.